{
  "abstract": "Current sequencing methods are inefficient and bottlenecked by repeated sampling of highly abundant molecules, which dominate sequencing reads, limit assay throughput and sensitivity for rare targets. For example, single-cell RNA sequencing (scRNA-seq) can profile up to millions of cells, but remains severely constrained by sequencing cost, resulting in shallow gene coverage and high dropout rate. Here we report an information science-inspired method, Compression Sequencing, that tackles this fundamental inefficiency and enables highly improved (>100x) sequencing power. Our method works by performing an accurate and unbiased logarithmic transform on molecular abundances over a wide (5 logs) dynamic range, thus suppressing high-abundance targets and enriching rare ones, while maintaining quantitative accuracy. Applied to scRNA-seq libraries, our method allows ultra-sensitive detection of low-abundance transcripts (2-5x more UMIs), ultra-low sequencing cost (200x reduction), preserves accurate cell types and differential expression analysis over a 500-2,000 gene panel. In AML clinical samples, Compression Sequencing reproduces clinical diagnosis and additionally allows transcriptomic profiling at affordable cost (est. $10 per sample). Our approach thus enables ultra-sensitive and scalable single-cell analysis for large-scale functional genomics studies, drug discovery screens, AI cell model training, as well as affordable single-cell disease diagnostics.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Bioengineering, Rice University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "Y. Yan"
    },
    {
      "affs": [
        0
      ],
      "name": "M. Dai"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dtce3yg1185k6.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.01.748706",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.01.748706v1.full",
  "id": "dtce3yg1185k6",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.01.748706v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioengineering"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7207595148",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.01.748706v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dtce3yg1185k6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:52Z",
      "native_id": "10.64898/2026.09.01.748706",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.01.748706v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioengineering",
      "subfield": "systems-synthetic"
    }
  ],
  "title": "Compression Sequencing enables ultra-sensitive and scalable scRNA-seq",
  "title_text": "Compression Sequencing enables ultra-sensitive and scalable scRNA-seq",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:52Z",
  "url": "https://research.draconitic.com/paper/dtce3yg1185k6",
  "venue": "bioRxiv",
  "versions": []
}